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Reading: Generalized Fractional Processes with Conditional Heteroscedasticity

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Generalized Fractional Processes with Conditional Heteroscedasticity

Authors:

Gnanadarsha Dissanayake ,

The University of Sydney, NSW, AU
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Shelton Peiris

School of Mathematics and Statistics, University of Sydney, AU
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Abstract

Generalized fractional processes in terms of Gegenbauer polynomials and GARCH (Generalized Autoregressive Conditional Heteroscedastic) errors is introduced and derived as a time series model. A related simulation study of the proposed model depicts statistical properties of the new class established in terms of the realization, sample autocorrelation function, the- oretical autocorrelation function, partial autocorrelation function and the spectral density function.

DOI: http://dx.doi.org/10.4038/sljastats.v12i0.4964

Sri Lankan Journal of Applied Statistics Vol.12 2011 pp.1-12

DOI: http://doi.org/10.4038/sljastats.v12i0.4964
How to Cite: Dissanayake, G. & Peiris, S., (2012). Generalized Fractional Processes with Conditional Heteroscedasticity. Sri Lankan Journal of Applied Statistics. 12, pp.1–12. DOI: http://doi.org/10.4038/sljastats.v12i0.4964
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Published on 02 Dec 2012.
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